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          <p>CodeQL是一种分析引擎，其首先将code转换为database，再通过类QL的方式挖掘漏洞。LGTM基于CodeQL提供在线服务。</p><h1 id="漏洞类型"><a href="#漏洞类型" class="headerlink" title="漏洞类型"></a>漏洞类型</h1><p>数据流分析和模式匹配类型漏洞</p><h1 id="覆盖语言"><a href="#覆盖语言" class="headerlink" title="覆盖语言"></a>覆盖语言</h1><p>• C/C++<br>• C#<br>• Go<br>• Java<br>• Python<br>• JavaScript（Includes JSX and Flow code, YAML, JSON, HTML, and XML files）<br>• TypeScript[New]<br>详情见：<a href="https://help.semmle.com/codeql/supported-languages-and-frameworks.html#" target="_blank" rel="noopener">https://help.semmle.com/codeql/supported-languages-and-frameworks.html#</a></p>
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          <h1 id="工具介绍"><a href="#工具介绍" class="headerlink" title="工具介绍"></a>工具介绍</h1><p>FlowDroid[1,2] 是 Arzt 等人设计的一款上下文敏感（对象敏感），流敏感，域敏感的静态污点分析工具，除此之外，与其他污点分析工具（如TAJ）不同的是其针对安卓应用，对安卓应用的特性做了很多改进和优化。</p><p>该工具基于Soot[3]和Heros[4]，soot作为经典的Java语言分析框架，能提供SSA和callgraph，Heros是一个通用的IFDS/IDS分析框架，速度没有WALA快，但是比WALA更通用。</p>
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          <h1 id="数据流分析的问题"><a href="#数据流分析的问题" class="headerlink" title="数据流分析的问题"></a>数据流分析的问题</h1><p>先前有很多数据流分析问题都是关于变量中的值分析，例如：符号分析、常量传播、污点分析等。考虑对以下CFG做数值分析：</p><p><img src="/pl-北大程序分析（SSA和稀疏分析）/image-20201002210707678.png" alt="image-20201002210707678" style="width:50%;"></p><ul>
<li>在空间复杂度方面，对每个节点都需要保存一份关于x, y, z的值，而一个节点通常只影响了少数变量（如节点2与y,z 无关）</li>
<li>在时间复杂度方面，对当节点一更新y时，更新至于节点3有关，但是数据流必须通过2才能传到3</li>
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          <h1 id="Soundiness"><a href="#Soundiness" class="headerlink" title="Soundiness"></a>Soundiness</h1><p>Soundiness是五年前的概念，学者们发现程序分析用于实际的现代语言中仍存在各种问题，这些问题是难以分析的语言特性导致的，因此提出Soundiness，说明如何处理这些难解的问题。</p><h2 id="Hard-features"><a href="#Hard-features" class="headerlink" title="Hard features"></a>Hard features</h2><blockquote>
<p>Hard-to-analyze features: an aggressively conservative treatment to these features will likely make the analysis too imprecise to scale,rendering the analysis useless.</p>
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          <h1 id="Feasible-and-Realizable-Paths"><a href="#Feasible-and-Realizable-Paths" class="headerlink" title="Feasible and Realizable Paths"></a>Feasible and Realizable Paths</h1><h2 id="In-Feasible-Paths"><a href="#In-Feasible-Paths" class="headerlink" title="(In)Feasible Paths"></a>(In)Feasible Paths</h2><blockquote>
<p><strong>Infeasible Paths:</strong> Paths in CFG that do not correspond to actual executions.</p>
</blockquote><p>程序在CFG中会存在不被执行到路径，这些路径会污染分析结果，给静态分析带来不利影响。</p>
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          <p>先前的分析算法虽然展示了分析细节，但是过于复杂难以实现，本课将介绍一种一种声明式语言（Datalog）以数据驱动推导的方式做指针分析和污点分析</p><h1 id="Motivation"><a href="#Motivation" class="headerlink" title="Motivation"></a>Motivation</h1><h2 id="Imperative-vs-Declarative"><a href="#Imperative-vs-Declarative" class="headerlink" title="Imperative vs Declarative"></a>Imperative vs Declarative</h2><p>Imperative（命令式）语言主要有Java、C/C++等，展示了详细的实现细节；</p>
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          <p>在软件安全中，最常见的漏洞有注入类漏洞（Injection）和信息泄露（Information Leak），而本节课将介绍的Information Flow 分析方法可以有效处理以上漏洞，具体来说，就是我们常用的污点分析技术。</p><h1 id="Information-Flow-Security"><a href="#Information-Flow-Security" class="headerlink" title="Information Flow Security"></a>Information Flow Security</h1>
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          <h1 id="Introduction"><a href="#Introduction" class="headerlink" title="Introduction"></a>Introduction</h1><h2 id="Problem-of-Context-Insensitive-C-I"><a href="#Problem-of-Context-Insensitive-C-I" class="headerlink" title="Problem of Context-Insensitive (C.I.)"></a>Problem of Context-Insensitive (C.I.)</h2><ul>
<li>每次函数调用时，调用的上下文不一样；</li>
<li>在不同的调用上下文里，函数内的变量会指向不同；</li>
<li>在上下文不敏感的指针分析中，函数内的对象指向的内容是不同上下文混合起来的（如上例中 $n$ 指向 $o_1$ 和 $o_2$），而这些内容会后向传播，造成更多不准确的结果。</li>
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          <h1 id="Motivation"><a href="#Motivation" class="headerlink" title="Motivation"></a>Motivation</h1><p>CHA 只看定义类型，会得到很多实际不被调用的子类方法，如下图所示，CHA的常量分析时只能得到NAC</p><p>而指针分析会得到更精确的对象类型：</p><p><img src="/pl-静态程序分析课程笔记（指针分析）/image-20200818213708639.png" alt="image-20200818213708639"></p><h1 id="Introduction-to-Pointer-Analysis"><a href="#Introduction-to-Pointer-Analysis" class="headerlink" title="Introduction to Pointer Analysis"></a>Introduction to Pointer Analysis</h1><blockquote>
<p>A fundamental static analysis, computes which memory locations a pointer can point to.</p>
<p>For object-oriented programs, computes which objects a pointer (variable or field) can point to.</p>
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          <h1 id="Motivation"><a href="#Motivation" class="headerlink" title="Motivation"></a>Motivation</h1><p>过程内分析遇到函数调用只能做保守假设，导致过多误报，而过程间分析可以跟进被调函数，因此更准确.</p><h1 id="Call-Graph-Construction"><a href="#Call-Graph-Construction" class="headerlink" title="Call Graph Construction"></a>Call Graph Construction</h1><blockquote>
<p> A call graph is a set of call edges from  call-sites to their target methods (callees)</p>
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